Generative models trained on a single image aim to generate new diverse samples, similar in appearance to the image/video on which they were trained.
Most notably, SinGAN [1]} and InGAN[2]} trained multi-scale GANs to learn the distribution of patches in an image. They showed its applicability to diverse random generation from a single image, as well as a variety of other image synthesis applications (inpainting, style transfer, etc.). Their results are usually better suited to synthesis from a single image than models trained on large collection of data.
More recently, GPNN [3]} showed that most image synthesis tasks proposed by single-image GAN-based models [4]}, [1]}, [2]} can be solved by classical non-parametric patch nearest-neighbour methods [7]}, [8]}, [9]}, and achieve outputs of higher quality while reducing generation time by orders of magnitude.
However, nearest-neighbour methods have a very limited notion of generalization, and are therefore limited to tasks where it is natural to "copy" parts of the input.
In this respect, learning based methods like SinGAN [1]}
still offer applicability like shown in the tasks of harmonization or animation.
